class_docstr: A model built on the DeepSet architecture.
code: def __init__(
      self,
      input_dim: int,
      hidden_dim: int,
      output_dim: int,
      encoder_dims: List[int],
      decoder_dims: List[int],
      dropout: float,
  ):
      super().__init__()
  
      self.encoder = _MLP(
          [input_dim] + encoder_dims + [hidden_dim], dropout
      )
      self.decoder = _MLP(
          [hidden_dim + 1] + decoder_dims + [output_dim], dropout
      )
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/surrogate/torch/deepset.py
imports: from typing import List
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new DeepSetModel object from inherited base classes: nn with example: obj = DeepSetModel(input_dim, hidden_dim, output_dim, encoder_dims, decoder_dims, dropout)